Neural Related Work Summarization with a Joint Context-driven Attention Mechanism
This addresses the problem of generating coherent related work sections for researchers, though it is incremental as it builds on existing seq2seq methods.
The paper tackled automatic related work summarization by developing a neural summarizer using a seq2seq paradigm with a joint context-driven attention mechanism to incorporate textual and graphic contexts, achieving considerable improvement over typical seq2seq and classical baselines on a large dataset.
Conventional solutions to automatic related work summarization rely heavily on human-engineered features. In this paper, we develop a neural data-driven summarizer by leveraging the seq2seq paradigm, in which a joint context-driven attention mechanism is proposed to measure the contextual relevance within full texts and a heterogeneous bibliography graph simultaneously. Our motivation is to maintain the topic coherency between a related work section and its target document, where both the textual and graphic contexts play a big role in characterizing the relationship among scientific publications accurately. Experimental results on a large dataset show that our approach achieves a considerable improvement over a typical seq2seq summarizer and five classical summarization baselines.